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Related Experiment Video

Updated: May 6, 2026

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

MuscleGolfAR: Embodied versus Detached Visualization for Motions and Inferred Muscle Activations in Augmented

Ruofan Liu, Chen-Chieh Liao, Takuya Takahashi

IEEE Transactions on Visualization and Computer Graphics

|April 3, 2026

Summary

This summary is machine-generated.

This study introduces MuscleGolfAR, an augmented reality system for golf training, using muscle activity and motion data. Embodied AR visualizations improved feedback ownership, while detached views aided whole-body understanding in golfers.

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Sports Science
Human-Computer Interaction
Biomechanics

Background:

Fine-grained motor skills like golf require precise kinematic and dynamic control.
Dynamic representations, particularly muscle activity, are crucial but underexplored in motor skill training.
Augmented reality (AR) offers potential for innovative training solutions.

Purpose of the Study:

To introduce MuscleGolfAR, an AR training system integrating swing motion and muscle activity for golf.
To develop a cost-effective method for inferring muscle activations using multimodal data.
To evaluate the effectiveness of embodied versus detached AR visualizations for motor skill training.

Main Methods:

Construction of a multimodal dataset including posture, electromyography (EMG), and plantar pressure data.
Development of an AR system (MuscleGolfAR) to display inferred EMG via embodied and detached visualizations.
Conducting user studies to assess the impact of different visualization strategies on training effectiveness.

Main Results:

Embodied AR visualizations enhanced user ownership of augmented feedback during golf swings.
Detached AR visualizations facilitated a more holistic comprehension of whole-body movement.
Individual preferences for visualization strategies correlated with practice habits and skill levels.

Conclusions:

MuscleGolfAR provides a novel approach to golf training by integrating biomechanical and muscle activity data.
Both embodied and detached AR visualizations offer distinct benefits for motor skill acquisition.
Personalized AR training system design should consider individual user characteristics for optimal effectiveness.